With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantita...With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.展开更多
Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature,but its linkage to precipitation variability has been less well discussed...Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature,but its linkage to precipitation variability has been less well discussed.This study shows that the snow water equivalent(SWE)over the Urals region in early(1–14)November is positively associated with precipitation in southern China during15–21 November and 6–15 January,based on the study period 1979/80–2016/17.In early November,a decreased Urals SWE warms the air locally via diabatic heating,indicative of significant land–atmosphere coupling over the Urals region.Meanwhile,a stationary Rossby wave train originates from the Urals and propagates along the polar-front jet stream.In mid(15–21)November,this Rossby wave train propagates downstream toward East Asia and,combined with the deepened East Asian trough,reduces the precipitation over southern China by lessening the water vapor transport.Thereafter,during 22 November to 5 January,there are barely any obvious circulation anomalies owing to the weak land–atmosphere coupling over the Urals.In early(6–15)January,the snowpack expands southward to the north of the Mediterranean Sea and cools the overlying atmosphere,suggestive of land–atmosphere coupling occurring over western Europe.A stationary Rossby wave train trapped in the subtropical westerly jet stream appears along with anomalous cyclonic circulation over Europe,and again with a deepened East Asian trough and less precipitation over southern China.The current findings have implications for winter precipitation prediction in southern China on the subseasonal timescale.展开更多
文摘With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.
基金supported by the National Key Research and Development Program of China grant number 2016YFA0600703the National Natural Science Foundation of China grant number 41875118+1 种基金Fei LI was supported by the RCN Nansen Legacy Project grant number 276730the Bjerknes Climate Prediction Unit with funding from the Trond Mohn Foundation grant number BFS2018TMT01。
文摘Evolution of the autumn snowpack has been considered as a potential source for the subseasonal predictability of winter surface air temperature,but its linkage to precipitation variability has been less well discussed.This study shows that the snow water equivalent(SWE)over the Urals region in early(1–14)November is positively associated with precipitation in southern China during15–21 November and 6–15 January,based on the study period 1979/80–2016/17.In early November,a decreased Urals SWE warms the air locally via diabatic heating,indicative of significant land–atmosphere coupling over the Urals region.Meanwhile,a stationary Rossby wave train originates from the Urals and propagates along the polar-front jet stream.In mid(15–21)November,this Rossby wave train propagates downstream toward East Asia and,combined with the deepened East Asian trough,reduces the precipitation over southern China by lessening the water vapor transport.Thereafter,during 22 November to 5 January,there are barely any obvious circulation anomalies owing to the weak land–atmosphere coupling over the Urals.In early(6–15)January,the snowpack expands southward to the north of the Mediterranean Sea and cools the overlying atmosphere,suggestive of land–atmosphere coupling occurring over western Europe.A stationary Rossby wave train trapped in the subtropical westerly jet stream appears along with anomalous cyclonic circulation over Europe,and again with a deepened East Asian trough and less precipitation over southern China.The current findings have implications for winter precipitation prediction in southern China on the subseasonal timescale.
